Generative AI

Month 1: Introduction to Generative AI

Week 1: Introduction to Generative AI

– What is Generative AI?

– Applications and Use Cases

Week 2: Probability and Statistics Basics

– Probability Distributions

– Descriptive Statistics

Week 3: Introduction to Machine Learning

– Supervised vs. Unsupervised Learning

– Regression vs. Classification

Week 4: Deep Learning Fundamentals

– Neural Networks Basics

– Activation Functions

– Loss Functions

Month 2: Generative Models and Applications

Week 5: Autoencoders

– Introduction to Autoencoders

– Encoder and Decoder Architecture

Week 6: Variational Autoencoders (VAEs)

– Introduction to VAEs

– Variational Inference

Week 7: Generative Adversarial Networks (GANs) – Part 1

– Introduction to GANs

– GAN Architecture

Week 8: Generative Adversarial Networks (GANs) – Part 2

– Training GANs

– Applications of GANs

Month 3: Advanced Topics and Future Directions

Week 9: Applications of Generative AI

– Image Generation with GANs

– Text Generation with Recurrent Neural Networks (RNNs):

– Style Transfer

– Anomaly Detection

Week 10: Ethical Considerations and Future Trends

– Future Directions in Generative AI

– Ethical Considerations

– Practical Exercises and Projects throughout the course to reinforce learning, with discussions on ethical considerations and future trends in Generative AI.